ebay-ml-lister/ebay_api.py

import json
import requests
import pandas as pd

class FindingApi:
    '''Some docstring to get rid of linting errors'''
    def __init__(self, service, pageNumber):
        self.service = [
            'findItemsAdvanced', 'findCompletedItems',
            'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory',
            'findItemsByProduct'
            ][service]
        self.pageNumber = list(range(1, pageNumber)) # 64 pages is recommended
        # this will give equal weights to cats given call restraints

    # departments = ["3034","93427"] (womens and mens)
    def get_data(self):
        '''# Gets raw JSON data fom FindingApi service call
        '''
        with open('cat_list.txt') as jf:
            cat_list = json.load(jf)
        for category_id in cat_list:
            for i in self.pageNumber:
                params = {
                    "OPERATION-NAME":self.service,
                    "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
                    "SERVICE-VERSION":"1.13.0",
                    "RESPONSE-DATA-FORMAT":"JSON",
                    "categoryId":category_id,
                    "paginationInput.entriesPerPage":"100",
                    "paginationInput.PageNumber":i
                    }
                response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1",
                        params=params)
                data = response.json()
            return data

    def get_ids_from_cats(self):
        '''
        Creates a 20-itemId list to use for the ShoppingApi
        call
        '''
        data = self.get_data()
        itemid_results_list = []
        try:# TODO run pdb here to see how to extract itemId before update_df
            training = pd.read_csv('training.csv')
            for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
                if item not in training.values not in itemid_results_list:# TODO need to figure out syntax to check if item_id is present in both list and training set. (list to avoid repeat results)
                    itemid_results_list.append(item['itemId'][0]) # itemId
                    # values are in lists for some reason
        except (pd.errors.EmptyDataError, FileNotFoundError):
            for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
                if item not in itemid_results_list:
                    itemid_results_list.append(item['itemId'][0])
        item_id_results = [','.join(itemid_results_list[n:n+20]) for n in list(range(0,
            len(itemid_results_list), 20))]

        return item_id_results

class ShoppingApi:
    '''
    Creates objects from ShoppingApi service calls that can interact with
    pandas dataframes
    '''
    def get_item_from_findItemsByCategory(self, item_id_results):
        '''
        Gets raw JSON data from multiple live listings given multiple itemIds
        '''
        for twenty_id in item_id_results:
            params = {
                "callname":"GetMultipleItems",
                "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
                "version":"671",
                "responseencoding":"JSON",
                "ItemID":twenty_id,
                "IncludeSelector":"ItemSpecifics",
                }

            response = requests.get("https://open.api.ebay.com/shopping?", params=params)
            data = response.json()
            return data # TODO save data as file??

class CurateData:
    '''
    Contains functions for curating data for machine learning training sets
    '''
    def update_df(self, data):
        '''
        Extracts itemIds and name-value list , creates new dict and appends df
        '''
        for item in data:
            names = []
            values = []
            nvl = data['Item'][0]['ItemSpecifics']['NameValueList']
            for nvl_dict in nvl:
                names.append(nvl_dict['Name'])
                values.append(nvl_dict['Value'])
            # TODO Also append itemId and value to the dictionary somewhere

            nvl_dict = dict(zip(names, values))
            data.update(nvl_dict)
        df = pd.json_normalize(data)
        df.to_csv('training.csv', mode='a')

def main():
    '''
    Main program creates/updates a csv file to use for ML training from live
    ebay listings
    '''
    service, pageNumber = input('service and pageNumber:').split()
    service = int(service)
    pageNumber = int(pageNumber)
    finding = FindingApi(service, pageNumber)
    item_id_results = finding.get_ids_from_cats()
    shopping = ShoppingApi()
    data = shopping.get_item_from_findItemsByCategory(item_id_results)
    curate = CurateData()
    curate.update_df(data)

if __name__ == "__main__":
    main()
# Limited to 5000 calls to shopping api per day, and getMultpileitems service maxes out at 20 items
# per call leaving you 100,000 items per day for you pandas dataframe initially. So you'll have
# to divide these up into the categories. This will leave you with about 6.25K results per cat.
# More than enough data for your dataset.

# Need to make sure dataframe gets important stuff outside of nvl in order to
# access values for cross referencing itemIds from calls
# Need to decide if list gets accessed from df or if you're just going to have
# list contents extracted and possibly placed into separate cells/labels